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Software Engineer from the University of Extremadura (UEX) with a master's degree in Artificial Intelligence from the International University of La Rioja (UNIR). Researcher in the area of remote sensing in the Quercus Software Engineering Group. Experience in the development of autonomous processes for the treatment and processing of multispectral satellite data and the development of neural networks. I'm interested in artificial intelligence and remote sensing to help with the sustainability and improvement of existing systems.
The demand for new tools for mass remote sensing of crops, combined with the open and free availability of satellite imagery, has prompted the development of new methods for crop classification. Because this classification is frequently required to be completed within a specific time frame, performance is also essential. In this work, we propose a new method that creates synthetic images by extracting satellite data at the pixel level, processing all available bands, as well as their data distributed over time considering images from multiple dates. With this approach, data from images of Sentinel-2 are used by a deep convolutional network system, which will extract the necessary information to discern between different types of crops over a year after being trained with data from previous years. Following the proposed methodology, it is possible to classify crops and distinguish between several crop classes while also being computationally low-cost. A software system that implements this method has been used in an area of Extremadura (Spain) as a complementary monitoring tool for the subsidies supported by the Common Agricultural Policy of the European Union.
Guillermo Siesto; Marcos Fernández-Sellers; Adolfo Lozano-Tello. Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks. Remote Sensing 2021, 13, 3378 .
AMA StyleGuillermo Siesto, Marcos Fernández-Sellers, Adolfo Lozano-Tello. Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks. Remote Sensing. 2021; 13 (17):3378.
Chicago/Turabian StyleGuillermo Siesto; Marcos Fernández-Sellers; Adolfo Lozano-Tello. 2021. "Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks." Remote Sensing 13, no. 17: 3378.